An improved neural network for convex quadratic optimization with application to real-time beamforming

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This paper develops an improved neural network to solve convex quadratic optimization problems with general linear constraints. Compared with the existing primal–dual neural network and dual neural network for solving such problems, the proposed neural network has a lower complexity for implementation. Unlike the Kennedy–Chua neural network, the proposed neural network can converge to an exact optimal solution. Analyzed results and illustrative examples show that the proposed neural network has a fast convergence to the optimal solution. Finally, the proposed neural network is effectively applied to real-time beamforming.